Abstract
Unmanned aerial vehicle (UAV) has been widely used in various fields, and meeting practical high-quality flight paths is one of the crucial functions of UAV. Many algorithms have the problem of too fast convergence and premature in UAV path planning. This study proposed a sparrow particle swarm algorithm for UAV path planning, the SPSA. The algorithm selects a suitable model for path initialization, changes the discoverer position update, and reinforces the influence of start-end line on path search, which can significantly reduce blind search. The number of target points reached is increased by adaptive variable speed escapes in areas of deadlock. In this case, the planned trajectory will fluctuate, and adaptive oscillation optimization can effectively reduce the fluctuation of the path. Finally, the optimal path is simplified, and the path nodes are interpolated with cubic splines to improve the smoothness of the path, which improves the smoothness of the UAV flight trajectory and makes it more suitable for use as the UAV real flight trajectory. By comparison, it can be concluded that the improved SPSA has good convergence speed and better search results and can avoid local optimality.
Highlights
Unmanned aerial vehicle (UAV) has been widely used in various fields, such as transportation, rescue, and military, with the continuous progress of technology
Traditional algorithms such as the A∗ algorithm [1], artificial potential field method [2, 3], and RRT [4] have been used for path planning, but most of them still suffer from high computational complexity and local convergence
Duan [15] et al proposed a UAV path planning based on an improved water droplet algorithm, which considers the effects of ice accumulation, Richardson number, meteorological changes, and different flight altitudes on UAV path planning. ey used the virtual potential field method to adjust the flight direction of the UAV to perform static path planning and dynamic path planning for the UAV
Summary
Unmanned aerial vehicle (UAV) has been widely used in various fields, such as transportation, rescue, and military, with the continuous progress of technology. UAVs are affected by buildings, weather, and their energy consumption during flight, so how to plan a safer, more efficient, and faster path is an ongoing hot research problem. Traditional algorithms such as the A∗ algorithm [1], artificial potential field method [2, 3], and RRT [4] have been used for path planning, but most of them still suffer from high computational complexity and local convergence. Phung [32] proposed an improved discrete particle swarm algorithm (DPSO) to be used in UAV path planning to improve the algorithm’s performance through qualitative initialization, random variation, and edge swapping by taking advantage of parallel computing.
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